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arXiv Computer Vision

FlyHack: Stress-Testing Mechanistic Claims in a Connectome-Constrained Visual Model

FlyHack examines how mechanistic claims in a connectome‑constrained visual model depend on the comparison model, intervention, circuit representation, and trained solution. Using a 50‑model ensemble of the Drosophila motion‑vision model (flyvis) and task‑matched causal CNN controls, the study finds that behavioral phenomena such as reverse‑phi reversal and stationary‑sawtooth responses vary with period, velocity, and model family, while adversarial robustness differs between flyvis and CNNs. At the mechanistic level, attribution accuracy and circuit conclusions also shift with representation choices and ensemble members, indicating that conclusions drawn from connectome‑constrained models are highly context‑dependent.

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Hugging Face Trending Papers
Oct 9

FlyHack: Stress-Testing Mechanistic Claims in a Connectome-Constrained Visual Model

FlyHack evaluates how mechanistic conclusions drawn from connectome-constrained neural networks depend on model choice, interventions, circuit representation, and training outcomes. Using a 50‑model ensemble of the flyvis motion‑vision system and task‑matched causal CNN controls, the study finds that key behavioral phenomena—such as reverse‑phi reversal and stationary‑sawtooth responses—vary across models and are not consistently reproduced by CNNs. At the mechanistic level, attribution accuracy and circuit conclusions also differ markedly with representation and ensemble member, indicating that mechanistic claims must be scoped to the specific model family and experimental conditions.

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arXiv Machine Learning
Oct 22

Reconstruction differences rival individual differences in the fly compass connectome and break trained circuit models

The study compares nine reconstructions of the Drosophila head‑direction circuit, including four different flies and five variants of the same fly with altered thresholds, proofreading, or synapse detection. It finds that changes within a single fly’s reconstruction can alter the circuit more than half the difference seen between different flies, and that neural‑network models trained on one wiring fail to transfer to others, with errors increasing dramatically when weights are perturbed. Despite these reconstruction differences, imaging data show that the biological compass remains functional and stable.

By Md Ali Ashraf Yad, Nazifa Tabassum Nabila
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arXiv Computer Vision
Oct 7

From the Drosophila Visual Connectome to General-Purpose Computer Vision

arXiv:2610.08418v1 Announce Type: new Abstract: Biological connectomes encode structured solutions to visual computation that may provide reusable inductive biases for artificial vision. We develop C...

By Zongyu Li, Akito Yamauchi, Huaizhi Liu, Vishwanatha Rao, Jia Guo, for the Frontotemporal Lobar Degeneration Neuroimaging Initiative, for the Alzheimer's Disease Neuroimaging Initiative
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arXiv AI
Sep 28

FLIP: Final Layer Inference-Time Probing for Vision-Language Models

FLIP is a final‑layer inference‑time probe designed to test whether a logit‑facing intervention site in an open‑weight vision‑language model (VLM) supports structured, task‑linked computation rather than generic perturbation. The probe applies elementwise flooring to the final normalized hidden state before logit computation, leaving other model components unchanged. By sweeping intervention strength on a controlled detection/counting task, FLIP identifies three regimes—negligible change, a bounded interior regime with improved detection recall and reduced counting error, and over‑suppression—while a four‑criterion protocol ensures the observed effects are mechanistically interpretable.

By Drandreb Earl O. Juanico, Rowel O. Atienza
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